Human values shape how we expect language models to behave.
Dominant alignment approaches treat values as global objectives, although the appropriate value orientation is personal and contextual.
Training-free alignment can steer models toward target values at inference time, yet has been largely evaluated in single-turn settings and with a single steering direction (e.g., toward safety).
We evaluate four prompt- and activation-based steering methods in multi-turn conversations, where user and assistant models push toward the same or opposing, equally desirable values.
We track alignment with a linear probe across three models and two value trade-offs.
When assistant and user pull in opposite directions, the side matching the assistant's baseline bias often prevails, and effects that look successful early often fade or reverse after a few turns.
Steering should therefore be evaluated over full conversations and in both directions of a value trade-off.